{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e4793ef5",
   "metadata": {},
   "source": [
    "# Ensembles notebook\n",
    "\n",
    "<a href=\"https://mybinder.org/v2/gh/tinkoff-ai/etna/master?filepath=examples/ensembles.ipynb\">\n",
    "    <img src=\"https://mybinder.org/badge_logo.svg\"  align='left'>\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4949ce7f",
   "metadata": {},
   "source": [
    "This notebook contains the simple examples of using the ensemble models with ETNA library.\n",
    "\n",
    "**Table of Contents**\n",
    "\n",
    "* [Load Dataset](#chapter1)  \n",
    "* [Build Pipelines](#chapter2)\n",
    "* [Ensembles](#chapter3)\n",
    "    * [VotingEnsemble](#section_3_1)\n",
    "    * [StackingEnsamble](#section_3_2)\n",
    "    * [Results](#section_3_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b9df4dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82360d8",
   "metadata": {},
   "source": [
    "## 1. Load Dataset <a class=\"anchor\" id=\"chapter1\"></a>\n",
    "\n",
    "In this notebook we will work with the dataset contains only one segment with monthly wine sales. Working process with the dataset containing more segments will be absolutely the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "639d0580",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from etna.datasets import TSDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "01e2fcee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "original_df = pd.read_csv(\"data/monthly-australian-wine-sales.csv\")\n",
    "original_df[\"timestamp\"] = pd.to_datetime(original_df[\"month\"])\n",
    "original_df[\"target\"] = original_df[\"sales\"]\n",
    "original_df.drop(columns=[\"month\", \"sales\"], inplace=True)\n",
    "original_df[\"segment\"] = \"main\"\n",
    "original_df.head()\n",
    "df = TSDataset.to_dataset(original_df)\n",
    "ts = TSDataset(df=df, freq=\"MS\")\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c879183c",
   "metadata": {},
   "source": [
    "## 2. Build Pipelines <a class=\"anchor\" id=\"chapter2\"></a>\n",
    "\n",
    "Given the sales' history, we want to select the best model(pipeline) to forecast future sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "68918585",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.pipeline.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ee58fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.pipeline import Pipeline\n",
    "from etna.models import (\n",
    "    NaiveModel,\n",
    "    SeasonalMovingAverageModel,\n",
    "    CatBoostMultiSegmentModel,\n",
    ")\n",
    "from etna.transforms import LagTransform\n",
    "from etna.metrics import MAE, MSE, SMAPE, MAPE\n",
    "\n",
    "HORIZON = 3\n",
    "N_FOLDS = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6815f49",
   "metadata": {},
   "source": [
    "Let's build four pipelines using the different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f0dc26e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "naive_pipeline = Pipeline(model=NaiveModel(lag=12), transforms=[], horizon=HORIZON)\n",
    "seasonalma_pipeline = Pipeline(\n",
    "    model=SeasonalMovingAverageModel(window=5, seasonality=12),\n",
    "    transforms=[],\n",
    "    horizon=HORIZON,\n",
    ")\n",
    "catboost_pipeline = Pipeline(\n",
    "    model=CatBoostMultiSegmentModel(),\n",
    "    transforms=[LagTransform(lags=[6, 7, 8, 9, 10, 11, 12], in_column=\"target\")],\n",
    "    horizon=HORIZON,\n",
    ")\n",
    "pipeline_names = [\"naive\", \"moving average\", \"catboost\"]\n",
    "pipelines = [naive_pipeline, seasonalma_pipeline, catboost_pipeline]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "106e3885",
   "metadata": {},
   "source": [
    "And evaluate their performance on the backtest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53c1a0b9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   15.4s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   30.1s remaining:   45.2s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   44.5s remaining:   29.7s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:  1.2min remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:  1.2min finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:   13.3s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   20.2s remaining:   30.4s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   27.0s remaining:   18.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   40.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   40.6s finished\n",
      "[Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.\n",
      "[Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    9.4s\n",
      "[Parallel(n_jobs=5)]: Done   2 out of   5 | elapsed:   16.7s remaining:   25.0s\n",
      "[Parallel(n_jobs=5)]: Done   3 out of   5 | elapsed:   23.5s remaining:   15.7s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   37.6s remaining:    0.0s\n",
      "[Parallel(n_jobs=5)]: Done   5 out of   5 | elapsed:   37.6s finished\n"
     ]
    }
   ],
   "source": [
    "metrics = []\n",
    "for pipeline in pipelines:\n",
    "    metrics.append(\n",
    "        pipeline.backtest(\n",
    "            ts=ts,\n",
    "            metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "            n_folds=N_FOLDS,\n",
    "            aggregate_metrics=True,\n",
    "            n_jobs=5,\n",
    "        )[0].iloc[:, 1:]\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "928e04bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive</th>\n",
       "      <td>2437.466667</td>\n",
       "      <td>1.089199e+07</td>\n",
       "      <td>9.949886</td>\n",
       "      <td>10.222106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moving average</th>\n",
       "      <td>1913.826667</td>\n",
       "      <td>6.113701e+06</td>\n",
       "      <td>7.897570</td>\n",
       "      <td>7.824056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>2271.766726</td>\n",
       "      <td>8.923741e+06</td>\n",
       "      <td>9.376638</td>\n",
       "      <td>10.013138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        MAE           MSE     SMAPE       MAPE\n",
       "naive           2437.466667  1.089199e+07  9.949886  10.222106\n",
       "moving average  1913.826667  6.113701e+06  7.897570   7.824056\n",
       "catboost        2271.766726  8.923741e+06  9.376638  10.013138"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.concat(metrics)\n",
    "metrics.index = pipeline_names\n",
    "metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9581f2a",
   "metadata": {},
   "source": [
    "## 3. Ensembles <a class=\"anchor\" id=\"chapter3\"></a>\n",
    "To improve the performance of the individual models, we can try to make ensembles out of them. Our library contains two ensembling methods, which we will try on now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e7e3e6",
   "metadata": {},
   "source": [
    "### 3.1 VotingEnsemble<a class=\"anchor\" id=\"section_3_1\"></a>\n",
    "\n",
    "`VotingEnsemble` forecasts future values with weighted averaging of it's `pipelines` forecasts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5338aeea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.ensembles import VotingEnsemble"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f7ee7db",
   "metadata": {},
   "source": [
    "By default, `VotingEnsemble` uses **uniform** weights for the pipelines' forecasts. However, you can specify the weights manually using the `weights` parameter. The higher weight the more you trust the base model. In addition, you can set `weights` with the literal `auto`. In this case, the weights of pipelines are assigned with the importances got from `feature_importance_` property of `regressor`.\n",
    "\n",
    "*Note*: The `weights` are automatically normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1c4029fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "voting_ensemble = VotingEnsemble(pipelines=pipelines, weights=[1, 9, 4], n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f1cb83b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>voting ensemble</th>\n",
       "      <td>1972.207943</td>\n",
       "      <td>6.685831e+06</td>\n",
       "      <td>8.172377</td>\n",
       "      <td>8.299714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         MAE           MSE     SMAPE      MAPE\n",
       "voting ensemble  1972.207943  6.685831e+06  8.172377  8.299714"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_ensamble_metrics = voting_ensemble.backtest(\n",
    "    ts=ts,\n",
    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "    n_folds=N_FOLDS,\n",
    "    aggregate_metrics=True,\n",
    "    n_jobs=2,\n",
    ")[0].iloc[:, 1:]\n",
    "voting_ensamble_metrics.index = [\"voting ensemble\"]\n",
    "voting_ensamble_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a26b503b",
   "metadata": {},
   "source": [
    "### 3.2 StackingEnsemble<a class=\"anchor\" id=\"section_3_2\"></a>\n",
    "`StackingEnsemble` forecasts future using the metamodel to combine the forecasts of it's `pipelines`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "78c46663",
   "metadata": {},
   "outputs": [],
   "source": [
    "from etna.ensembles import StackingEnsemble"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b430668",
   "metadata": {},
   "source": [
    "By default, `StackingEnsemble` uses only the pipelines' forecasts as features for the `final_model`. However, you can specify the additional features using the `features_to_use` parameter. The following values are possible:\n",
    "+ **None** - use only the pipelines' forecasts(default)\n",
    "+ **List[str]** - use the pipelines' forecasts + features from the list\n",
    "+ **\"all\"** - use all the available features\n",
    "\n",
    "*Note:* It is possible to use only the features available for the base models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "273626b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "stacking_ensemble_unfeatured = StackingEnsemble(pipelines=pipelines, n_folds=10, n_jobs=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "272cc433",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:    9.3s finished\n",
      "/Users/a.p.chikov/Library/Caches/pypoetry/virtualenvs/etna-ts-5LkX4_TY-py3.8/lib/python3.8/site-packages/joblib/parallel.py:775: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  n_jobs = self._backend.configure(n_jobs=self.n_jobs, parallel=self,\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
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      "[Parallel(n_jobs=4)]: Done   3 out of   3 | elapsed:    0.8s finished\n",
      "[Parallel(n_jobs=4)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
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     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stacking ensemble</th>\n",
       "      <td>2058.487868</td>\n",
       "      <td>8.182131e+06</td>\n",
       "      <td>8.508705</td>\n",
       "      <td>8.50082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MAE           MSE     SMAPE     MAPE\n",
       "stacking ensemble  2058.487868  8.182131e+06  8.508705  8.50082"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacking_ensamble_metrics = stacking_ensemble_unfeatured.backtest(\n",
    "    ts=ts,\n",
    "    metrics=[MAE(), MSE(), SMAPE(), MAPE()],\n",
    "    n_folds=N_FOLDS,\n",
    "    aggregate_metrics=True,\n",
    "    n_jobs=2,\n",
    ")[0].iloc[:, 1:]\n",
    "stacking_ensamble_metrics.index = [\"stacking ensemble\"]\n",
    "stacking_ensamble_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "051a0ba0",
   "metadata": {},
   "source": [
    "In addition, it is also possible to specify the `final_model`. You can use any regression model with the sklearn interface for this purpose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c975d5c5",
   "metadata": {},
   "source": [
    "### 3.3 Results<a class=\"anchor\" id=\"section_3_3\"></a>\n",
    "Finally, let's take a look at the results of our experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c2f1d397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MAE</th>\n",
       "      <th>MSE</th>\n",
       "      <th>SMAPE</th>\n",
       "      <th>MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive</th>\n",
       "      <td>2437.466667</td>\n",
       "      <td>1.089199e+07</td>\n",
       "      <td>9.949886</td>\n",
       "      <td>10.222106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moving average</th>\n",
       "      <td>1913.826667</td>\n",
       "      <td>6.113701e+06</td>\n",
       "      <td>7.897570</td>\n",
       "      <td>7.824056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catboost</th>\n",
       "      <td>2271.766726</td>\n",
       "      <td>8.923741e+06</td>\n",
       "      <td>9.376638</td>\n",
       "      <td>10.013138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>voting ensemble</th>\n",
       "      <td>1972.207943</td>\n",
       "      <td>6.685831e+06</td>\n",
       "      <td>8.172377</td>\n",
       "      <td>8.299714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stacking ensemble</th>\n",
       "      <td>2058.487868</td>\n",
       "      <td>8.182131e+06</td>\n",
       "      <td>8.508705</td>\n",
       "      <td>8.500820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           MAE           MSE     SMAPE       MAPE\n",
       "naive              2437.466667  1.089199e+07  9.949886  10.222106\n",
       "moving average     1913.826667  6.113701e+06  7.897570   7.824056\n",
       "catboost           2271.766726  8.923741e+06  9.376638  10.013138\n",
       "voting ensemble    1972.207943  6.685831e+06  8.172377   8.299714\n",
       "stacking ensemble  2058.487868  8.182131e+06  8.508705   8.500820"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics = pd.concat([metrics, voting_ensamble_metrics, stacking_ensamble_metrics])\n",
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9378bcf5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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